Haptic technology is a tactile feedback technology which takes advantage of the sense of touch by applying forces, vibrations, or motions to the user. Haptic feedback may be reproduced in a device through the use of electromagnetic technologies such as vibratory motors, capacitive actuation, or by other methods.
Artificial neural networks (ANNs) are computational models inspired by human central nervous systems that are capable of machine learning and pattern recognition. ANNs are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. ANNs generally include sets of adaptive weights, i.e., numerical parameters that are tuned by a learning algorithm. The adaptive weights are, conceptually, connection strengths between “neurons,” which are activated during training and prediction.
Once an ANN has been structured for a particular application, the network may be trained. To start the training process, initial weights are chosen via an educated guess of the user, or even randomly. In supervised training, both the inputs and the desired outputs are provided to the ANN. The ANN then processes the inputs and compares its results against the desired outputs. If the results do not agree, errors can be propagated back through the ANN, causing the ANN to adjust the weights. With increasing amounts of data (i.e., inputs and desired outputs), the ANN refines chosen weights. When the ANN has been adequately trained, the weights can be frozen, or the ANN can continue to learn and refine while in use.